Final Degree Project. Degree in Industrial Engineering. University of Seville.

This repository hosts the files of the project Deep Learning Applications to Optimise an EV Charging Station related with the EV charging demand prediction.

• Data processing: Public data processing and preparing in order to feed the time series models.
• Training and benchmarking: Train different neural network models on the gathered data and evaluate and compare their performance on the value prediction task.
• Deployment: Build test functions to make inferences and predictions on new data

The content is arranged in different folders:

• Root folder: contains basic train.py script, also contains a train_demo.ipynb and test_demo.ipynb jupyter notebooks to get started into training models and making inferences.
• data_preprocessing\: contains files related to first data processing and visualization. Also contains the datasets in raw_data, preprocessed_data, processed_data

├── data_handler.py
├── data_preprocessing
│   ├── data_preprocessing.py
│   ├── data_processing.ipynb
│   ├── preprocessed_data
│   │   ├── final_2018
│   │   ├── final_2019
│   │   ├── final_2019_2020.pkl
│   │   └── final_2020
│   ├── processed_data
│   │   ├── data2019.csv
│   │   ├── data2019.json
│   │   └── data2019.pkl
│   └── raw_data
│       ├── acndata_sessions_2019.json
│       └── acndata_sessions_2020.json
├── figures
│   └── Figure 2022-03-22 200750.png
├── LICENSE
├── model_utils.py
├── plot_utils.py
├── README.md
├── test_demo.ipynb
├── train_demo.ipynb
└── train.py

Please refer to installation

• Model benchmarking
  • Main results for each model in Results directory